DISTAL: The Distributed Tensor Algebra Compiler
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Abstract (arXiv)
We introduce DISTAL, a compiler for dense tensor algebra that targets modern distributed and heterogeneous systems. DISTAL lets users independently describe how tensors and computation map onto target machines through separate format and scheduling languages. The combination of choices for data and computation distribution creates a large design space that includes many algorithms from both the past (e.g., Cannon's algorithm) and the present (e.g., COSMA). DISTAL compiles a tensor algebra domain specific language to a distributed task-based runtime system and supports nodes with multi-core CPUs and multiple GPUs. Code generated by DISTAL is competitive with optimized codes for matrix multiply on 256 nodes of the Lassen supercomputer and outperforms existing systems by between 1.8x to 3.7x (with a 45.7x outlier) on higher order tensor operations.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Mar 2022.
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Citing papers per year: 2023: 1; 2024: 4; 2025: 1.
6 papers.
- 2025WaferLLM: Large Language Model Inference at Wafer Scalecited by 13
- 2024Improving Parallel Program Performance with LLM Optimizers via Agent-System Interfacescited by 11
- 2024PartIR: Composing SPMD Partitioning Strategies for Machine Learningcited by 10
- 2024Scaling Deep Learning Computation over the Inter-Core Connected Intelligence Processor with T10cited by 5
- 2023Serving Deep Learning Model in Relational Databasescited by 2
- 2024Masked Matrix Multiplication for Emergent Sparsitycited by 0
- 2025WaferLLM: Large Language Model Inference at Wafer Scale6 Feb 2025
- 2024Improving Parallel Program Performance with LLM Optimizers via Agent-System Interfaces21 Oct 2024
- 2024Scaling Deep Learning Computation over the Inter-Core Connected Intelligence Processor with T109 Aug 2024
- 2024Masked Matrix Multiplication for Emergent Sparsity21 Feb 2024
- 2024PartIR: Composing SPMD Partitioning Strategies for Machine Learning20 Jan 2024
- 2023Serving Deep Learning Model in Relational Databases7 Oct 2023